Wavelet Domain LMMSE-Like Denoising Algorithm Based on GGD ML Estimation

Li Jun-xia; Shui Peng-lang

doi:10.3724/SP.J.1146.2006.00531

Volume 29 Issue 12

Jan. 2011

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2007 > 29(12): 2853-2857

Li Jun-xia, Shui Peng-lang. Wavelet Domain LMMSE-Like Denoising Algorithm Based on GGD ML Estimation[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2853-2857. doi: 10.3724/SP.J.1146.2006.00531

Citation:

Li Jun-xia, Shui Peng-lang. Wavelet Domain LMMSE-Like Denoising Algorithm Based on GGD ML Estimation[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2853-2857. doi: 10.3724/SP.J.1146.2006.00531

Citation:

PDF( 362 KB)

Wavelet Domain LMMSE-Like Denoising Algorithm Based on GGD ML Estimation

doi: 10.3724/SP.J.1146.2006.00531

Received Date: 2006-04-21
Rev Recd Date: 2006-10-16
Publish Date: 2007-12-19

Abstract

Abstract

Based on the assumption that wavelet coefficients obey Generalized Gaussian Distribution (GGD), this paper adopts Maximum Likelihood (ML) principle to estimate wavelet coefficients variance of common images in sub-bands. The proposed estimator is product of a sub-band adjustable factor and a power mean factor. Compared to the recently proposed SI-AdaptShr, LAWMAP and other wavelet-based methods, better de-noising results may be obtained for the proposed method. Furthermore, a simplified algorithm is also formed to de-speckle SAR images. It is shown that the new method may remarkably reduce the calculation amount and helpful for the post-processing of large scale SAR images.
- S,
- A,
- R

FullText(HTML)

References(1)

References

[1] Donoho D L. De-noising by soft-thresholding[J].IEEE Trans. on Inform. Theory.1995, 41(5):613-627 [2] Donoho D L and Johnstone I M. Ideal spatial adaption via wavelet shrinkage[J].Biometrika.1994, 81(3):425-455 [3] Donoho D L and Johnstone I M. Adapting to unknown smoothness via wavelet shrinkage[J].Journal of the American Statistical Assoc.1995, 90(432):1200-1224 [4] Donoho D L and Johnstone I M. Wavelet shrinkage: Asymptopia? J. R. Stat. Soc. B, Ser. B, 1995, 57(2): 301-369. [5] Chang S G, Yu B, and Vetterli M. Adaptive wavelet thresholding for image denoising and compression[J].IEEE Trans. on Image Processing.2000, 9(9):1532-1546 [6] Chang S G, Yu B, and Vetterli M. Spatially adaptive wavelet thresholding with context modeling for image denoising[J].IEEE Trans. on Image Processing.2000, 9(9):1522-1531 [7] Mihcak M K, Kozintsev I K, and Ramchandran K, et al.. Low-complexity image denoising based on statistical modeling of wavelet coefficients[J].IEEE Signal Processing Letters.1999, 6(12):300-303 [8] Varanasi M and Aazhang B. Parametric generalized gaussian density estimation[J].J.Acoust.Soc.Am.1989, 86(4):1404-1415 [9] Portilla J, Strela V, and Wainwright M J, et al.. Image denoising using scale mixture of Gaussians in the wavelet domain[J].IEEE Trans. on Image Processing.2003, 12(11):1338-1351 [10] Starck J, Candes E, and Donoho D L. The curvelet transform for image denoising. IEEE Trans. on Image Processing, 2002 11(6): 670-684. [11] Pennec E and Mallat S. Sparse geometric image representations with bandelets[J].IEEE Trans. on Image Processing.2005, 14(4):423-438 [12] Shui, Peng-Lang. Image denoising algorithm via doubly local Wiener filtering with directional windows in wavelet domain[J].IEEE Signal Processing Letters.2005, 12(10):681-684 [13] Westerink P H, Biemond J, and Boekee D E. An optimal bit allocation algorithm for sub-band coding. Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, Dallas, Apr.1987: 1378-1381. [14] Simoncelli E and Adelson E. Noise removal via Bayesian wavelet coring[J].Proc. IEEE Int. Conf. Image Processing, Sept.1996, Vol.1:379-382 [15] Lopes A, Nezry E, and Touzi R, et al.. Maximum a posteriori filtering and first order texture models in SAR images. AGARSS, 1990: 2409-2412.

Relative Articles

Supplements(0)

Cited By

Proportional views